Artificial selection of communities drives the emergence of structured interactions

Abstract

Species-rich communities, such as the microbiota or microbial ecosystems, provide key functions for human health and climatic resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. Numerical simulations explored the evolutionary dynamics of this complex, multi-scale system. However, a comprehensive theoretical understanding of the process of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of communities composed of a large number of interacting species, described by disordered generalized Lotka-Volterra equations. Our analytical and numerical results reveal that selection for total community abundance leads to increased levels of mutualism and interaction diversity. Correspondingly, the interaction matrix acquires a specific structure that is generic for selection of collective functions. Our approach moreover allows to disentangle the role of different control parameters in determining the efficiency of the selection process, and can thus be used as a guidance in optimizing artificial selection protocols.